Large quantities of ecological and environmental data are increasingly available thanks to initiatives sponsoring the collection of large-scale data and efforts to increase the publication of already collected datasets. As a result, progress in ecology is increasingly limited by the speed at which we can organize and analyze data. To help improve ecologists' ability to quickly access and analyze data we have been developing software that designs database structures for ecological datasets and then downloads the data, pre-processes it, and installs it into major database management systems (at the moment we support MySQL, PostgreSQL, SQLite, and Microsoft Access).

Once the EcoData Retriever has loaded the data into the database it is easy to connect to the database using standard tools (e.g., MS Access, Filemaker, etc.).The EcoData Retriever can download and install small datasets in seconds and large datasets in minutes. The program also cleans up known issues with the datasets and automatically restructures them into a format appropriate for standard database management systems. The automation of this process reduces the time for a user to get most large datasets up and running by hours, and in some cases days.

Precompiled binaries the most recent release are available for Windows, OS X, and Ubuntu/Debian at the project website.

To install the EcoData Retriever from source, you'll need Python 2.6+ with the following packages installed:

• wxPython
• xlrd

###The following packages are optional

• PyMySQL (for MySQL)
• sqlite3 (for SQLite)
• psycopg2 (for PostgreSQL)
• pyodbc (for MS Access - this option is only available on Windows)

###To install from source

1. Clone the repository
2. From the directory containing setup.py, run the following command: python setup.py install
3. After installing, type retriever from a command prompt to launch the EcoData Retriever

After installing, run retriever update to download all of the available dataset scripts. To see the full list of command line options and datasets run retriever --help. The output will look like this:

usage: retriever [-h] [-v] [-q] {install,update,gui,new,ls,citation,help} ...

positional arguments:
  {install,update,gui,new,ls,citation,help}
                        sub-command help
    install             download and install dataset
    update              download updated versions of scripts
    gui                 launch retriever in graphical mode
    new                 create a new sample retriever script
    ls                  display a list all available dataset scripts
    citation            view citation
    help

optional arguments:
  -h, --help            show this help message and exit
  -v, --version         show program's version number and exit
  -q, --quiet           suppress command-line output

To install datasets, use retriever install:

usage: retriever install [-h] [--compile] [--debug]
                         {mysql,postgres,sqlite,msaccess,csv} ...

positional arguments:
  {mysql,postgres,sqlite,msaccess,csv}
                        engine-specific help
    mysql               MySQL
    postgres            PostgreSQL
    sqlite              SQLite
    msaccess            Microsoft Access
    csv                 CSV

optional arguments:
  -h, --help            show this help message and exit
  --compile             force re-compile of script before downloading
  --debug               run in debug mode

###Examples

These examples are using Breeding Bird Survey data (BBS) 

Using Install

  'retriever install -h   (gives install options)`

Using specific database engine, retriever install {Engine}

  `retriever install mysql -h     (gives install mysql options)`
  `retriever install mysql --user myuser --password ******** --host localhost --port 8888 --database_name testdbase BBS'

install data into an sqlite database named mydatabase.db you would use:

  `retriever install sqlite BBS -f mydatabase.db`

Using download

  `retriever download -h    (gives you help options)
  `retriever download BBS"
  `retriever download BBS --path C:\Users\Documents

Using citation
  `retriever citation   (citation of the retriever engine)`
  `retriever citation BBS   (citation of BBS data)` 

Development of this software was funded by the Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative through Grant GBMF4563 to Ethan White and the National Science Foundation as part of a CAREER award to Ethan White.